Achieving Reproducibility and Replicability of Molecular Dynamics and Monte Carlo Simulations Using the Molecular Simulation Design Framework (MoSDeF).

Molecular simulations are increasingly used to predict thermophysical properties and explore molecular-level phenomena beyond modern imaging techniques. To make these tools accessible to nonexperts, several open-source molecular dynamics (MD) and Monte Carlo (MC) codes have been developed. However, using these tools is challenging, and concerns about the validity and reproducibility of the simulation data persist.

MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC.

MoSDeF-GOMC is a python interface for the Monte Carlo software GOMC to the Molecular Simulation Design Framework (MoSDeF) ecosystem. MoSDeF-GOMC automates the process of generating initial coordinates, assigning force field parameters, and writing coordinate (PDB), connectivity (PSF), force field parameter, and simulation control files. The software lowers entry barriers for novice users while allowing advanced users to create complex workflows that encapsulate simulation setup, execution, and data analysis in a single script.

High-throughput screening of tribological properties of monolayer films using molecular dynamics and machine learning.

Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties.

Examining the self-assembly of patchy alkane-grafted silica nanoparticles using molecular simulation.

In this work, molecular dynamics simulations are used to examine the self-assembly of anisotropically coated "patchy" nanoparticles. Specifically, we use a coarse-grained model to examine silica nanoparticles coated with alkane chains, where the poles of the grafted nanoparticle are bare, resulting in strongly attractive patches. Through a systematic screening process, the patchy nanoparticles are found to form dispersed, string-like, and aggregated phases, dependent on the combination of alkane chain length, coating chain density, and the fractional coated surface area.

Towards Molecular Simulations that are Transparent, Reproducible, Usable By Others, and Extensible (TRUE).

Systems composed of soft matter (e.g., liquids, polymers, foams, gels, colloids, and most biological materials) are ubiquitous in science and engineering, but molecular simulations of such systems pose particular computational challenges, requiring time and/or ensemble-averaged data to be collected over long simulation trajectories for property evaluation.

MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer Films.

We demonstrate how the recently developed Python-based Molecular Simulation and Design Framework (MoSDeF) can be used to perform molecular dynamics screening of functionalized monolayer films, focusing on tribological effectiveness. MoSDeF is an open-source package that allows for the programmatic construction and parametrization of soft matter systems and enables TRUE (transferable, reproducible, usable by others, and extensible) simulations. The MoSDeF-enabled screening identifies several film chemistries that simultaneously show low coefficients of friction and adhesion.